from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data
mnist  = input_data.read_data_sets("/tmp/data/",one_hot=True)

learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

n_input = 28
n_steps = 28

n_hidden = 128
n_classes = 10

x = tf.placeholder("float",[None,n_steps,n_input])
y = tf.placeholder("float",[None,n_classes])

weights = {
    'out':tf.Variable(tf.random_normal([2*n_hidden,n_classes]))
}
biases = {
    'out':tf.Variable(tf.random_normal([n_classes]))
}
def BiRNN(X,weights,biases):
    x = tf.unstack(x,n_steps,1)
    lstm_tw_cell = rnn.BasicLSTMCell(n_hidden,forget_bias=1.0)
    lstm_bw_cell = rnn.BasicLSTMCell(n_hidden,forget_bias=1.0)

    try:
        outputs,_,_ = rnn.stack_bidirectional_rnn(lstm_bw_cell,lstm_tw_cell,x,dtype = tf.float32)
    except:
        outputs = rnn.stack_bidirectional_rnn(lstm_tw_cell,lstm_bw_cell,x,dtypr=tf.float32)
pred = BiRNN(x,weights,biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init = tf.global_variables_initializer()

with tf.Seesion() as sess:
    sess.run(init)
    step = 1
    while step *batch_size < training_iters:
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        batch_x = batch_x.reshape((batch_size,n_steps,n_input))
        sess.run(optimizer,feed_dict={x:batch_x,y:batch_yh})
        if step % display_step == 0:
            acc = sess.run(accuracy,feed__dict={x:batch_x,y:batch_y})
            loss = sess.run(cost,feed_dict={xL:batch_x,y:batch_y})
            print("Iter"+str(step*batch_size))
        step+=1

test_len =128
test_data = mnist.test.images[:test_len].reshape((-1,n_steps,n_input))
test_label = mnist.test.labels[:test_len]
print("test Accuracy",
      sess.run(accuracy,feed_dict={x:test_data,y:test_label}))